Basic concepts

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Basic concepts

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Educational visualization, set different parameters, number of hidden layers

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Curated implementations of machine learning models in pytorch, tensor flow, caffe2, computer vision, natural language processing

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Collection of interactive machine learning examples on classification, unsupervised, recurrent nets, generative, basic ml, images & video, sounds & music, text & language

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Javascript library for training Deep Learning models (Neural Networks) entirely in your browser

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Variety of resources and pointers to information about Deep Learning

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Dissecting papers, explanation of the intuition and theory,aggregating resources, computer vision, deep learning, natural language processing

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Discussion of advanced topics and concepts

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FIve-course specialization, programming assignments

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High-level neural networks API, running on top of TensorFlow, CNTK, or Theano

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Open source deep learning platform that provides a seamless path from research prototyping to production deployment

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Open-source, numerical computation

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Concept videos showcasing the intuition behind Deep Learning methods, how to's, reviews of software libraries, applications, interviews

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Artificial neural networks demystified, overfitting, regularization, data collection and preprocessing, convolutional neural networks, transfer learning optimization tricks, recurrent neural networks, deep unsupervised learning, generative adversarial networks

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Deep learning, deep reinforcement learning, autonomous vehicles, and artificial intelligence

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Foundations of deep learning

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Practical introduction on how to build deep learning applications, image-classifier, cloud

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introductory course on deep learning methods, deep sequence modeling, intro to tensorflow; music generation, deep computer vision, deep generative modeling, facial recognition systems, deep reinforcement learning, limitations and new frontiers, pixels-to-control learning

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Building TensorFlow Applications, Deep Networks and Sequence Models, Convolutional Networks, Industry Applications

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Series of concept videos explaining the idea behind every Deep Learning method

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open-ended machine learning research

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Human-centered AI and autonomous vehicles

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Core concepts behind neural networks and deep learning

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Concepts, algorithm and applications with Scikit-Learn, Keras, and TensorFlow

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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.

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“What is Deep Learning?” - Chapter 1 from Deep Learning with Python by François Chollet “Fundamental concepts: how do machines learn?” - Chapter 2 from Grokking Deep Learning by Andrew Trask “Sequence-to-sequence models for chatbots” – Chapter 11 from Machine Learning with TensorFlow by Nishant Shukla with Kenneth Fricklas

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threshold logic

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Minimizing the cost function to train deep neural networks

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Concept videos showcasing the intuition behind Deep Learning methods, how to's, reviews of software libraries, applications, interviews

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Variety of resources and pointers to information about Deep Learning

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It defines the output of that node, or "neuron," given an input or set of inputs, it maps the resulting values into the desired range such as between 0 to 1 or -1 to 1

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Linear (identity) for all positive values, and zero for all negative values, popular for deep neural networks

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Dissecting papers, explanation of the intuition and theory,aggregating resources, computer vision, deep learning, natural language processing

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Loss function

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Discussion of advanced topics and concepts

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A technique for improving the speed, performance, and stability of artificial neural networks. It is used to normalize the input layer by adjusting and scaling the activations

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Core concepts behind neural networks and deep learning

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*
*

FIve-course specialization, programming assignments

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*
*

Concepts, algorithm and applications with Scikit-Learn, Keras, and TensorFlow

*Upvote*
*
*

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.

*Upvote*
*
*

Foundations of deep learning

*Upvote*
*
*

threshold logic

*Upvote*
*
*

Practical introduction on how to build deep learning applications, image-classifier, cloud

*Upvote*
*
*

Artificial neural networks demystified, overfitting, regularization, data collection and preprocessing, convolutional neural networks, transfer learning optimization tricks, recurrent neural networks, deep unsupervised learning, generative adversarial networks

*Upvote*
*
*

Building TensorFlow Applications, Deep Networks and Sequence Models, Convolutional Networks, Industry Applications

*Upvote*
*
*

Series of concept videos explaining the idea behind every Deep Learning method

*Upvote*
*
*

“What is Deep Learning?” - Chapter 1 from Deep Learning with Python by François Chollet “Fundamental concepts: how do machines learn?” - Chapter 2 from Grokking Deep Learning by Andrew Trask “Sequence-to-sequence models for chatbots” – Chapter 11 from Machine Learning with TensorFlow by Nishant Shukla with Kenneth Fricklas

*Upvote*
*
*

Educational visualization, set different parameters, number of hidden layers

*Upvote*
*
*

High-level neural networks API, running on top of TensorFlow, CNTK, or Theano

*Upvote*
*
*

Open source deep learning platform that provides a seamless path from research prototyping to production deployment

*Upvote*
*
*

Curated implementations of machine learning models in pytorch, tensor flow, caffe2, computer vision, natural language processing

*Upvote*
*
*

Open-source, numerical computation

*Upvote*
*
*

Collection of interactive machine learning examples on classification, unsupervised, recurrent nets, generative, basic ml, images & video, sounds & music, text & language

*Upvote*
*
*

Javascript library for training Deep Learning models (Neural Networks) entirely in your browser

*Upvote*
*
*

*Upvote*
*
*

Deep learning, deep reinforcement learning, autonomous vehicles, and artificial intelligence

*Upvote*
*
*

Human-centered AI and autonomous vehicles

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*
*

introductory course on deep learning methods, deep sequence modeling, intro to tensorflow; music generation, deep computer vision, deep generative modeling, facial recognition systems, deep reinforcement learning, limitations and new frontiers, pixels-to-control learning

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*
*

open-ended machine learning research

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*
*

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